Rotor unmanned aerial vehicle blade length-width ratio estimation method based on subband characteristics

Abstract

The invention discloses a rotor unmanned aerial vehicle blade length-width ratio estimation method based on subband characteristics, and belongs to the field of laser radar application detection. The method comprises the following steps: step 1, constructing a multi-point scattering single-blade echo model of the rotor unmanned aerial vehicle, and performing theoretical derivation on rotor blade subband generation; 2, performing simulation verification on the subband characteristics of the rotor blade; 3, performing width estimation and error analysis on the rotor blade based on the subband characteristics; and 4, estimation and error analysis of the length-width ratio of the rotor blade based on the sub-band characteristics are carried out. According to the method, the width and the length-width ratio of the rotor unmanned aerial vehicle blade can be effectively estimated, a theoretical basis is provided for high-accuracy identification of the rotor unmanned aerial vehicle, and a technical support is provided for high-precision identification of the rotor unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention belongs to the field of laser radar application detection, and in particular relates to a method for estimating the aspect ratio of rotor UAV blades based on sub-frequency band characteristics. Background Art

[0002] The core parameters for drone identification include the rotation frequency of the rotor, the rotor length, the number of blades, and the number of rotors. These parameters play an important role in identifying rotary-wing drones, helping to determine the type, size, and other key features of the drone. However, existing rotary-wing drone identification technologies usually rely on a limited set of parameters, and these parameters are limited by current detection methods. Therefore, it is difficult for existing technologies to fully capture the multi-dimensional characteristics of drones, especially when faced with drones of different types and specifications. This limitation is more obvious. For example, microwave detection, as a common drone identification technology, has limited resolution, which makes it difficult to accurately distinguish the subtle differences in targets. For drones with small or complex structures, microwave detection often cannot provide enough details, resulting in a decrease in recognition accuracy. Therefore, adopting a high-precision detection method to obtain more dimensional rotary-wing drone parameter information is of great significance for the classification and identification of drones.

[0003] Current technology is relatively mature in terms of the rotation frequency and number of blades of drones, but there are still significant challenges in the field of high-precision identification of blade width and aspect ratio. Since the blade designs of different types of drones have subtle differences, the accuracy of identifying these subtle differences is extremely high. Therefore, how to accurately extract the parameters of drone blade width and aspect ratio in complex environments has become one of the key issues in today's research field. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problems existing in the background technology, and propose a method for estimating the aspect ratio of rotorcraft UAV blades based on sub-frequency band characteristics.

[0005] The present invention utilizes a large amount of rotor detail information contained in a high spatial resolution lidar echo signal, and realizes high recognition rate extraction of characteristic parameters such as the width and aspect ratio of the rotor UAV blades through peak time shift features.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for estimating the aspect ratio of a rotorcraft UAV blade based on sub-frequency band characteristics, the method comprising the following steps:

[0008] Step 1: Construction of the multi-point scattering single blade echo model of the rotor UAV and theoretical derivation of the rotor blade sub-band generation;

[0009] Step 2: Simulation verification of rotor blade sub-band characteristics;

[0010] Step 3: Width estimation and error analysis of rotor blades based on sub-frequency band characteristics;

[0011] Step 4: Estimation and error analysis of rotor blade aspect ratio based on sub-band characteristics;

[0012] Furthermore, in step 1, the construction of the multi-point scattering single blade echo model of the rotor UAV and the theoretical derivation of the rotor blade sub-band generation specifically include the following steps:

[0013] Step 1.1: Construction of the multi-point scattering single blade echo model of the rotorcraft UAV;

[0014] The rotor blade rotation center is established and located at the coordinate origin O, the azimuth angle α = 0°, the pitch angle is β, the distance from the laser radar to the rotor blade rotation center is R0, the rotor blade height is z0, and the distance from the scattering point on the rotor blade to the rotor blade rotation center is l M , R0>>l M , the scattering point rotates around the Z axis at a constant rotation speed ω; let the initial phase of point M on the rotor blade be The rotor blade reaches point M' after time t, and its phase is

[0015] The rotor blade is equivalent to a cuboid model with thickness, and the cuboid model is meshed. The points on the mesh are called scattering points, which replace the center point of the rotor blade scattering surface. The coordinates of each scattering point are (x i ,y j ,z k ), where i, j, k are integers; the distance from the rotor blade rotation center to each scattering point is l M (x,y,z)=(x 2 +y 2 +z 2 ) 1 / 2 , the rotor blade echo signal is:

[0016]

[0017] Where: R'0=R0+l M (x, y, z), f0 represents the carrier frequency of the laser radar, t represents time, λ represents wavelength, R M (t) represents the distance between the point M on the rotor target established by the rotor and the radar at time t, and σ represents the scattering rate;

[0018] Step 1.2: Theoretical derivation of rotor blade sub-band generation;

[0019] To simplify the calculation, the laser radar is equivalent to point P, the rotation center of the rotor blade is point O, the straight line PO is parallel to the laser radar line of sight, and the radar line of sight has an elevation angle of β; the distance from the laser radar P to the rotation center O of the rotor blade is r0, and the distance from P to the top point A of the rotor blade is r1; θ is the angle between the straight line OP and OA; θ′ is the angle between the velocity vector at point A and AP;

[0020] In the triangle ΔAOP, the sine theorem shows that:

[0021]

[0022] The velocity v of point A A The component vector in the AP direction is v AP =v A cosθ′, then:

[0023]

[0024] The micro-Doppler frequency shift when the rotor blade moves to point A is:

[0025]

[0026] Assuming that the aspect ratio of the rotor blades is not less than 4, and assuming that the rotation radius of the scattering point at the top of the rotor blade is equal to the blade length, then v A =ωL, formula (4) is rewritten to obtain:

[0027]

[0028] Since the rotor chord length is much smaller than the distance between the laser radar and the drone, it is approximately considered that: but:

[0029]

[0030] It can be seen from formula (6) that the micro-Doppler frequency shift generated by each scattering point on the rotor blade is related to ω and θ, and the rotation speed of the rotor is unchanged; when θ = (2n + 1)π / 2, the micro-Doppler frequency shift reaches the maximum; when θ = nπ, the micro-Doppler frequency shift is the minimum and is zero, where n is a positive integer, taking values ​​of 1, 2, 3..., therefore, the time-frequency diagram shows periodic changes;

[0031] Since the rotor blade has a certain width, points D and E are the two vertices of the blade, and the phase change corresponds to the angle between OD and OE. Since the velocity directions of points D and E are always different, there is always a velocity component in the laser radar line of sight, which generates a sub-band; the sub-band expression is:

[0032]

[0033] Where: W represents the width of the rotor blade.

[0034] Furthermore, in step 2, the rotor blade echo signal is simulated; the specific process is:

[0035] The vertices D and E of the rotor blades are the points on the rotor blades that are farthest from the modeling center of rotation. When the direction of point D in the rotor blade is perpendicular to the direction of the laser radar line of sight, the generated micro-Doppler frequency shift is zero, while other scattering points on the rotor blades still have a certain angle relative to the direction of the laser radar line of sight, and micro-Doppler frequency shift is generated. As the rotor blades rotate, when the line segment DE is perpendicular to the direction of the radar line of sight, the speeds of points D and E relative to the direction of the radar line of sight, that is, the radial speeds, are equal in magnitude and opposite in direction, and the micro-Doppler frequency shifts formed are equal in magnitude and opposite in direction, which manifests as a sub-band.

[0036] Furthermore, in step 3, the width estimation and error analysis of the rotor blade based on the sub-frequency band characteristics are specifically performed as follows:

[0037] Step 31: Estimating the width of the rotor blade based on the sub-frequency band characteristics;

[0038] According to the characteristics of the sub-frequency band, the process of extracting the width of the rotor blade is as follows: first, the rotor blade edge extraction result is divided into upper and lower parts along the fundamental frequency, and then the upper part is searched to extract the discontinuous maximum value f u , and the minimum value f of the gradient discontinuity in the lower half b , therefore, the formula for obtaining the sub-band is:

[0039] f W-max =f u -f b ;

[0040] The width W of the rotor blade is estimated based on formula (10):

[0041]

[0042] It can be determined from formula (10) that the estimated value of the width of the rotor blade is related to the pitch angle;

[0043] Step 32: Rotor blade error analysis based on sub-frequency band characteristics;

[0044] The absolute error of the width of the rotor blade estimated by the sub-band method is linearly related to the actual width, with a slope of 0.02 and a residual sum of squares of 4.39×10 -8 , the maximum absolute error is 0.0012m; the relative error of the width of the rotor blade estimated by the sub-band method fluctuates around 2.17%, and the maximum relative error is 2.4%.

[0045] Furthermore, in step 4, the estimation and error analysis of the rotor blade aspect ratio based on the sub-frequency band characteristics are specifically performed as follows:

[0046] Step 41: Estimation of rotor blade aspect ratio based on sub-frequency band characteristics;

[0047] The rotor blade aspect ratio is expressed as:

[0048]

[0049]

[0050] Where: f L-max The distance from point D to the center of rotation of the rotor blade is L OD The maximum frequency shift produced when

[0051] It can be determined from formula (11) that the aspect ratio of the rotor blade is equivalent to the ratio of the corresponding micro-Doppler maximum frequency shift, which is independent of the pitch angle parameter;

[0052] Step 42: Error analysis of rotor blade aspect ratio based on sub-frequency band characteristics;

[0053] Since the absolute error of the estimated aspect ratio is approximately in an exponential relationship with the true aspect ratio, when the aspect ratio does not exceed 6:1, the absolute error increases rapidly as the aspect ratio increases. After the aspect ratio exceeds 8:1, it fluctuates around 0.17, and the maximum absolute error does not exceed 0.2; the relative error fluctuates between 1% and 2.2%, and is less than 2.2%.

[0054] The beneficial effects of the present invention compared with the prior art are as follows: based on the laser micro-Doppler effect, the present invention proposes a method for estimating the aspect ratio of rotorcraft UAV blades based on sub-band characteristics, which can effectively estimate the width and aspect ratio of rotorcraft UAV blades, provides a theoretical basis for the high-accuracy identification of rotorcraft UAVs, and provides technical support for the high-precision identification of rotorcraft UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a diagram of the relative positions of the rotor and the laser line of sight.

[0056] Figure 2 is the time-frequency diagram of the rotor simulation width.

[0057] Figure 3 This is a flow chart of estimating rotor blade width using the sub-band method.

[0058] Figure 4 is an estimate of the width of the rotor blades.

[0059] Figure 5 is the error plot of the width estimation error.

[0060] Figure 6 is an estimate of the aspect ratio.

[0061] Figure 7 is the error plot of the aspect ratio estimation error.

[0062] Figure 8 This is a diagram of the laser micro-Doppler coherent detection integrated system.

[0063] Fig. 9 This is the experimental result diagram.

[0064] Fig.10 This is a map of the locations of the LiDAR and the UAV rotors. DETAILED DESCRIPTION

[0065] Specific implementation method 1: This implementation method discloses a method for estimating the aspect ratio of a rotorcraft drone blade based on sub-frequency band characteristics, and the method comprises the following steps:

[0066] Step 1: Construction of the multi-point scattering single blade echo model of the rotor UAV and theoretical derivation of the rotor blade sub-band generation (providing a theoretical basis for subsequent simulation verification and parameter estimation);

[0067] Step 2: Simulation verification of rotor blade sub-band characteristics (verify the correctness of theoretical derivation through simulation to ensure that the model and theory can accurately reflect the physical characteristics of rotor blades);

[0068] Step 3: Estimation and error analysis of rotor blade width based on sub-frequency band characteristics (using the time-frequency diagram to extract the rotation speed of the sub-frequency band and estimate the width of the rotor blade; analyzing the errors in the estimation process and evaluating the accuracy and reliability of the method);

[0069] Step 4: Estimation of the rotor blade aspect ratio based on sub-band characteristics (independent of the pitch angle, improving the versatility of the estimation) and error analysis (analyzing the error in the aspect ratio estimation to ensure the accuracy of the estimation result).

[0070] Furthermore, the parameter estimation of the width and aspect ratio of the UAV rotor blades is experimentally verified to ensure the effectiveness of the method in practical applications.

[0071] Furthermore, in step 1, the construction of the multi-point scattering single blade echo model of the rotor UAV and the theoretical derivation of the rotor blade sub-band generation specifically include the following steps:

[0072] Step 1.1: Construction of the multi-point scattering single blade echo model of the rotorcraft UAV;

[0073] like Fig.10As shown, the rotor blade rotation center is established and located at the coordinate origin O, the azimuth angle α = 0°, the pitch angle is β, the distance from the laser radar to the rotor blade rotation center is R0, the rotor blade height is z0, and the distance from the scattering point on the rotor blade to the rotor blade rotation center is l M , R0>>l M , the scattering point rotates around the Z axis at a constant rotation speed ω; let the initial phase of point M on the rotor blade be The rotor blade reaches point M' after time t, and its phase is

[0074] The rotor blade is equivalent to a cuboid model with thickness, and the cuboid model is meshed. The points on the mesh are called scattering points, which replace the center point of the rotor blade scattering surface. The coordinates of each scattering point are (x i ,y j ,z k ), where i, j, k are integers; the distance from the rotor blade rotation center to each scattering point is l M (x,y,z)=(x 2 +y 2 +z 2 ) 1 / 2 , the rotor blade echo signal is:

[0075]

[0076] Where: R'0=R0+l M (x, y, z), f0 represents the carrier frequency of the laser radar, t represents time, λ represents wavelength, R M (t) represents the distance between the point M on the rotor target established by the rotor and the radar at time t, and σ represents the scattering rate;

[0077] Formula (1) is the established multi-point scattering single blade echo model of the rotor UAV;

[0078] Step 1.2: Theoretical derivation of rotor blade sub-band generation;

[0079] The relative position of the rotor and the laser line of sight is as follows: Figure 1 As shown in the figure, the distance between the laser radar and the target rotor is much greater than the rotor length L, and the laser can be seen as a parallel light (such as Figure 1 Parallel lines with arrows on the middle right);

[0080] To simplify the calculation, the laser radar is equivalent to point P, the rotation center of the rotor blade is point O, the straight line PO is parallel to the laser radar line of sight, and the radar line of sight has an elevation angle of β; the distance from the laser radar P to the rotation center O of the rotor blade is r0, and the distance from P to the top point A of the rotor blade is r1; θ is the angle between the straight line OP and OA; θ′ is the angle between the velocity vector at point A and AP (such as Figure 1 shown);

[0081] In the triangle ΔAOP, the sine theorem shows that:

[0082]

[0083] The velocity v of point A A The component vector in the AP direction is v AP =v A cosθ′, then:

[0084]

[0085] The micro-Doppler frequency shift when the rotor blade moves to point A is:

[0086]

[0087] Assuming that the aspect ratio of the rotor blades is not less than 4, and assuming that the rotation radius of the scattering point at the top of the rotor blade is equal to the blade length, then v A =ωL, formula (4) is rewritten to obtain:

[0088]

[0089] Since the rotor chord length is much smaller than the distance between the laser radar and the drone, it is approximately considered that: but

[0090]

[0091] It can be seen from formula (6) that the micro-Doppler frequency shift generated by each scattering point on the rotor blade is related to ω and θ, and the rotation speed of the rotor is unchanged; when θ = (2n + 1)π / 2, the micro-Doppler frequency shift reaches the maximum; when θ = nπ, the micro-Doppler frequency shift is the minimum and is zero, where n is a positive integer, taking values ​​of 1, 2, 3..., therefore, the time-frequency diagram shows periodic changes;

[0092] Since the rotor blade has a certain width, points D and E are the two vertices of the blade, and the phase change corresponds to the angle between OD and OE. Since the velocity directions of points D and E are always different, there is always a velocity component in the laser radar line of sight, which generates a sub-band; the sub-band expression is:

[0093]

[0094] Where: W represents the width of the rotor blade.

[0095] Furthermore, in step 2, the rotor blade echo signal is simulated; the specific process is:

[0096] From the perspective of micro-Doppler frequency shift, the reason for the generation of the time-frequency diagram is explained. The vertices D and E of the rotor blades are the points on the rotor blades that are farthest from the modeling center of rotation. When the direction of point D in the rotor blade is perpendicular to the direction of the laser radar line of sight, the generated micro-Doppler frequency shift is zero, while other scattering points on the rotor blades still have a certain angle relative to the direction of the laser radar line of sight, and micro-Doppler frequency shift is generated (for example: the speed of point E is still generated by micro-Doppler frequency shift). As the rotor blades rotate, when line segment DE is perpendicular to the direction of the radar line of sight, the speeds of points D and E relative to the direction of the radar line of sight, that is, the radial speeds, are equal in magnitude and opposite in direction, and the micro-Doppler frequency shifts formed are equal in magnitude and opposite in direction, which manifests as a sub-band.

[0097] Furthermore, in step 3, the width estimation and error analysis of the rotor blade based on the sub-frequency band characteristics are specifically performed as follows:

[0098] Step 31: Estimating the width of the rotor blade based on the sub-frequency band characteristics;

[0099] According to the time-frequency diagram, the rotation speed of the sub-frequency band can be extracted. According to the characteristics of the sub-frequency band, the process of extracting the width of the rotor blade is as follows: first, the rotor blade edge extraction result is divided into upper and lower parts along the fundamental frequency, and then the upper part is searched to extract the discontinuous maximum value f u , and the minimum value f of the gradient discontinuity in the lower half b , therefore, the formula for obtaining the sub-band is:

[0100] f W-max =f u -f b ;

[0101] The width W of the rotor blade is estimated based on formula (10):

[0102]

[0103] It can be determined from formula (10) that the estimated value of the width of the rotor blade is related to the pitch angle (however, in reality, the pitch angle is generally unknown, so it is difficult to directly estimate the width of the rotor blade);

[0104] Step 32: Rotor blade error analysis based on sub-frequency band characteristics;

[0105] The absolute error of the width of the rotor blade estimated by the sub-band method is linearly related to the actual width, with a slope of 0.02 and a residual sum of squares of 4.39×10 -8 , the maximum absolute error is 0.0012m; the relative error of the width of the rotor blade estimated by the sub-band method fluctuates around 2.17%, and the maximum relative error is 2.4%, which verifies that this method has high accuracy.

[0106] Furthermore, in step 4, the estimation and error analysis of the rotor blade aspect ratio based on the sub-frequency band characteristics are specifically performed as follows:

[0107] Step 41: Estimation of rotor blade aspect ratio based on sub-frequency band characteristics;

[0108] The rotor blade aspect ratio is expressed as:

[0109]

[0110] Where: f L-max The distance from point D to the center of rotation of the rotor blade is L OD The maximum frequency shift produced when

[0111] It can be determined from formula (11) that the aspect ratio of the rotor blade is equivalent to the ratio of the corresponding micro-Doppler maximum frequency shift, which is independent of the pitch angle parameter (that is, the aspect ratio of the rotor can also be solved when the pitch angle is unknown);

[0112] Step 42: Error analysis of rotor blade aspect ratio based on sub-frequency band characteristics;

[0113] Since the absolute error of the estimated value of the aspect ratio is approximately exponentially related to the true aspect ratio, when the aspect ratio does not exceed 6:1, the absolute error increases rapidly as the aspect ratio increases. When the aspect ratio exceeds 8:1, it fluctuates around 0.17, and the maximum absolute error does not exceed 0.2; the relative error fluctuates between 1% and 2.2%, and is less than 2.2%. The estimation effect of the rotor aspect ratio parameters using the sub-band method is good and reliable.

[0114] Furthermore, in step 5, the parameter estimation experiment of the width and aspect ratio of the UAV rotor blade is verified; the specific process is:

[0115] The laser heterodyne interference experiment is adopted, that is, the laser beam emitted by the seed laser with an emission power of 100mW is divided into two parts, signal light and reference light, by the fiber beam splitter. The signal light with 90% of the total power is irradiated to the rotor blade target by the transmitting optical system and scattered, and the scattered micro-Doppler echo signal enters the fiber combiner through the receiving optical system; the reference light enters the acousto-optic modulator and shifts the frequency by 70MHz, and the output light enters the fiber coupler through the fiber attenuator; the two beams are mixed in the fiber coupler and transmitted to the balanced detector to form heterodyne interference and retain the difference frequency part, and the optical signal is converted into an electrical signal and collected by the acquisition card, and finally processed by the computer to obtain the time-frequency diagram; in the experiment, a direct current (DC) motor is used to drive the rotor blade to rotate, the driving voltage is set to 26V, and the corresponding rotation frequency is 6.5Hz. The experimental results show that the relative error of the width and aspect ratio of the rotor blade estimated by the sub-band is less than 5% (although the error is higher than the relative error obtained by simulation, the experiment proves that the laser radar system (i.e. the experimental platform built) can effectively detect the width and aspect ratio of the blade).

[0116] The laser wavelength is 1064nm to ensure the measurement accuracy of the lidar system.

[0117] Embodiment 1:

[0118] This embodiment discloses a method for estimating the blade aspect ratio of a rotorcraft drone based on sub-frequency band characteristics, the method comprising the following steps:

[0119] Step 1: Figure 1 As shown, the distance from point D to the center of the circle is L OD , the maximum frequency shift produced is:

[0120]

[0121] Since the absolute error of the line segment lengths OD and OE relative to the length of the rotor blade is very small, the line segment lengths OD and OE can be equivalent to the length of the rotor blade, that is, L OD =L; the above formula is modified to:

[0122]

[0123] When point D satisfies θ=±nπ, the frequency shift of this point relative to the laser is zero, and the length of the rotor blade contributes very little to the frequency shift. However, at this time, the speeds corresponding to other points on the width of the rotor blade still produce frequency shifts relative to the radar. When DE is perpendicular to OP, that is, the width of the rotor blade is perpendicular to the radar line of sight, the width of the rotor blade contributes the most to the frequency shift, and the frequency shift band generated at this time is a sub-band. The expression for the sub-band is:

[0124]

[0125] The width of the rotor blade is expressed as:

[0126]

[0127] The rotor blade aspect ratio can be expressed as:

[0128]

[0129] Step 2: Table 1 shows the simulation parameters of the rotor single blade echo signal

[0130] Table 1 Rotor single blade echo signal simulation parameters

[0131]

[0132] The generation of sub-bands is determined by the width of the rotor blades. Figure 2 It is obvious that there are sub-frequency band characteristics in the time-frequency diagram. From formula (10), we can see that the wider the width of the rotor blade, the wider the sub-frequency band generated;

[0133] Step 3: Assuming the rotor blade rotation frequency is f0 = 4 Hz, λ = 1064 nm and β = 30°; it can be seen that the maximum frequency shift f of the band micro-Doppler W-max It is linearly related to the width of the rotor blade. The width of the rotor blade is estimated based on formula (10) as follows: Figure 4 As shown in Figure 2, it is confirmed that the above algorithm can correctly estimate the width of the rotor blade. The error of estimating the width of the rotor blade is as follows: Figure 5 As shown in the figure, the absolute error of the width of the rotor blade estimated by the sub-band method is linearly related to the actual width, with a slope of 0.02 and a sum of squares of the residuals (SSR) of 4.39×10 -8 , the maximum absolute error is 0.0012m, the relative error of the width estimated by the sub-band method fluctuates around 2.17%, and the maximum relative error is 2.4%, which verifies that this method has high accuracy;

[0134] Step 4: Perform simulation calculations as follows to obtain the estimated rotor blade aspect ratio using the sub-band method. Figure 6 and Figure 7 As shown, the straight line in the figure is the linear fitting result, the slope of the straight line is 1.01, and the residual sum of squares is 0.0422. Figure 7 The red curve is the absolute error fitting curve, and the green straight line is the relative error fitting curve. Figure 7It can be seen that the absolute error of the estimated value of the aspect ratio is approximately exponentially related to the true aspect ratio. When the aspect ratio does not exceed 6:1, the absolute error increases rapidly as the aspect ratio increases. After the aspect ratio exceeds 8:1, it fluctuates around 0.17, the maximum absolute error does not exceed 0.2, and the relative error fluctuates between 1% and 2.2%, and is less than 2.2%. In summary, the sub-band method used in the present invention has a good effect on estimating the aspect ratio parameters of the rotor blades and is highly reliable.

[0135] Step 5: The parameters used in the experiment are shown in Table 2;

[0136] Table 2 Experimental parameters of UAV rotor laser coherence detection

[0137]

[0138] In the experiment, a direct current (DC) motor was used to drive the blades to rotate. The driving voltage was set to 26V, and the corresponding rotation frequency was 6.5Hz. Fig. 9 is the time-frequency diagram obtained in the experiment. Due to the imperfect surface of the rotor blade and the noise in the LiDAR system (i.e., the experimental platform built), the color distribution of each period TF diagram is not completely consistent. Therefore, we use a Gaussian filter for noise reduction and smoothing to improve edge extraction. It can be seen that the relative error of the width and aspect ratio of the rotor blade estimated using the sub-band in this experiment is less than 5%. Although the error is higher than the relative error obtained by simulation, the experiment proves that the LiDAR system (i.e., the experimental platform built) can effectively detect the width and aspect ratio of the rotor blade.

[0139] Table 3 Parameter extraction results

[0140]

[0141] The core parameters for drone identification include the rotation frequency of the rotor, the rotor length, the number of blades, and the number of rotors. These parameters play an important role in identifying rotary-wing drones, helping to determine the type, size, and other key features of the drone. However, existing rotary-wing drone identification technologies usually rely on a limited set of parameters, and these parameters are limited by the current detection methods. Therefore, it is difficult for existing technologies to fully capture the multi-dimensional characteristics of drones, especially when facing drones of different types and specifications. This limitation is more obvious. For example, although microwave detection has advantages in many fields, it also has some disadvantages. Its resolution is limited and it is difficult to accurately distinguish the details of small or complex targets, especially when identifying smaller drones. Laser detection has extremely high resolution and can accurately capture the details of tiny objects, especially in complex environments. The recognition effect of small drones is particularly significant. In addition, laser detection has a fast response speed, which can achieve real-time monitoring and rapid target identification, thereby improving the overall efficiency of the lidar system (i.e., the experimental platform built).

[0142] Figure 3 This is a flowchart of the sub-band method for estimating the width of the rotor blade. First, the echo signal is obtained according to the established model, and then the signal is converted into a time-frequency diagram through short-time Fourier transform (STFT), and Gaussian filtering and smoothing are performed. Next, Canny edge detection is used to extract edge features from the time-frequency diagram to obtain the sub-band f W-max and the rotation speed ω. Finally, the width W of the rotor blade is calculated by formula (10).

[0143] Figure 8 This is a diagram of the integrated system of laser micro-Doppler coherent detection. The system uses a 1064nm narrow linewidth bias-maintaining pulsed fiber laser as the laser source. After the 90:10 fiber beam splitter, the optical pulse is divided into two optical signals. The light with a power of 10% is used as the reference light and modulated by an acousto-optic modulator to produce a frequency shift of 70MHz. The light with a power of 90% is used as the signal light and transmitted to the area where the rotor of the drone is located through one of the two fiber collimators. The echo signal light of the rotor is then received by the other fiber collimator. The power of the reference light is adjusted by the fiber attenuator to make it equivalent to the power of the echo signal light. The Thorlabs PB460 balanced detector is selected for the detection part. The reference light and the echo signal light are input into a 50:50 fiber coupler. After mixing, they are divided into two inputs to the balanced detector to complete the photoelectric conversion. The optical signal is converted into an intermediate frequency electrical signal with a micro-Doppler frequency shift.

[0144] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms of assembly without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for estimating the blade aspect ratio of a rotorcraft drone based on sub-frequency band characteristics, characterized in that: The method comprises the following steps: Step 1: Construction of the multi-point scattering single blade echo model of the rotor UAV and theoretical derivation of the rotor blade sub-band generation; Step 2: Simulation verification of rotor blade sub-band characteristics; Step 3: Width estimation and error analysis of rotor blades based on sub-frequency band characteristics; Step 4: Estimation and error analysis of rotor blade aspect ratio based on sub-band characteristics.

2. The method for estimating the blade aspect ratio of a rotorcraft drone based on sub-frequency band characteristics according to claim 1, characterized in that: In step 1, the construction of the multi-point scattering single blade echo model of the rotor UAV and the theoretical derivation of the rotor blade sub-band generation specifically include the following steps: Step 1.1: Construction of the multi-point scattering single blade echo model of the rotorcraft UAV; The rotor blade rotation center is established and located at the coordinate origin O, the azimuth angle α = 0°, the pitch angle is β, the distance from the laser radar to the rotor blade rotation center is R0, the rotor blade height is z0, and the distance from the scattering point on the rotor blade to the rotor blade rotation center is l M , R0>>l M , the scattering point rotates around the Z axis at a constant rotation speed ω; let the initial phase of point M on the rotor blade be The rotor blade reaches point M' after time t, and its phase is The rotor blade is equivalent to a cuboid model with thickness, and the cuboid model is meshed. The points on the mesh are called scattering points, which replace the center point of the rotor blade scattering surface. The coordinates of each scattering point are (x i ,y j ,z k ), where i, j, k are integers; the distance from the rotor blade rotation center to each scattering point is l M (x,y,z)=(x 2 +y 2 +z 2 ) 1 / 2 , the rotor blade echo signal is: Where: R'0=R0+l M (x, y, z), f0 represents the carrier frequency of the laser radar, t represents time, λ represents wavelength, R M (t) represents the distance between the point M on the rotor target established by the rotor and the radar at time t, and σ represents the scattering rate; Step 1.2: Theoretical derivation of rotor blade sub-band generation; To simplify the calculation, the laser radar is equivalent to point P, the rotation center of the rotor blade is point O, the straight line PO is parallel to the laser radar line of sight, and the radar line of sight has an elevation angle of β; the distance from the laser radar P to the rotation center O of the rotor blade is r0, and the distance from P to the top point A of the rotor blade is r1; θ is the angle between the straight line OP and OA; θ′ is the angle between the velocity vector at point A and AP; In the triangle ΔAOP, the sine theorem shows that: The velocity v of point A A The component vector in the AP direction is v AP =v A cosθ′, then: The micro-Doppler frequency shift when the rotor blade moves to point A is: Assuming that the aspect ratio of the rotor blades is not less than 4, and assuming that the rotation radius of the scattering point at the top of the rotor blade is equal to the blade length, then v A =ωL, formula (4) is rewritten to obtain: Since the rotor chord length is much smaller than the distance between the laser radar and the drone, it is approximately considered that: but: It can be seen from formula (6) that the micro-Doppler frequency shift generated by each scattering point on the rotor blade is related to ω and θ, and the rotation speed of the rotor is unchanged; when θ = (2n + 1)π / 2, the micro-Doppler frequency shift reaches the maximum; when θ = nπ, the micro-Doppler frequency shift is the minimum and is zero, where n is a positive integer, taking values ​​of 1, 2, 3..., therefore, the time-frequency diagram shows periodic changes; Since the rotor blade has a certain width, points D and E are the two vertices of the blade, and the phase change corresponds to the angle between OD and OE. Since the velocity directions of points D and E are always different, there is always a velocity component in the laser radar line of sight, which generates a sub-band; the sub-band expression is: Where: W represents the width of the rotor blade.

3. The method for estimating the blade aspect ratio of a rotorcraft drone based on sub-frequency band characteristics according to claim 2, characterized in that: In step 2, the rotor blade echo signal is simulated; the specific process is: The vertices D and E of the rotor blades are the points on the rotor blades that are farthest from the modeling center of rotation. When the direction of point D in the rotor blade is perpendicular to the direction of the laser radar line of sight, the generated micro-Doppler frequency shift is zero, while other scattering points on the rotor blades still have a certain angle relative to the direction of the laser radar line of sight, and micro-Doppler frequency shift is generated. As the rotor blades rotate, when the line segment DE is perpendicular to the direction of the radar line of sight, the speeds of points D and E relative to the direction of the radar line of sight, that is, the radial speeds, are equal in magnitude and opposite in direction, and the micro-Doppler frequency shifts formed are equal in magnitude and opposite in direction, which manifests as a sub-band.

4. The method for estimating the blade aspect ratio of a rotorcraft based on sub-frequency band characteristics according to claim 1, characterized in that: In step 3, the width estimation and error analysis of the rotor blade based on the sub-frequency band characteristics are performed; the specific steps are as follows: Step 31: Estimating the width of the rotor blade based on the sub-frequency band characteristics; According to the characteristics of the sub-frequency band, the process of extracting the width of the rotor blade is as follows: first, the rotor blade edge extraction result is divided into upper and lower parts along the fundamental frequency, and then the upper part is searched to extract the discontinuous maximum value f u , and the minimum value f of the gradient discontinuity in the lower half b , therefore, the formula for obtaining the sub-band is: f W-max =f u -f b ; The width W of the rotor blade is estimated based on formula (10): It can be determined from formula (10) that the estimated value of the width of the rotor blade is related to the pitch angle; Step 32: Rotor blade error analysis based on sub-frequency band characteristics; The absolute error of the width of the rotor blade estimated by the sub-band method is linearly related to the actual width, with a slope of 0.02 and a residual sum of squares of 4.39×10 -8 , the maximum absolute error is 0.0012m; the relative error of the width of the rotor blade estimated by the sub-band method fluctuates around 2.17%, and the maximum relative error is 2.4%.

5. The method for estimating the blade aspect ratio of a rotorcraft drone based on sub-frequency band characteristics according to claim 4, characterized in that: In step 4, the estimation and error analysis of the rotor blade aspect ratio based on the sub-frequency band characteristics are as follows: Step 41: Estimation of rotor blade aspect ratio based on sub-frequency band characteristics; The rotor blade aspect ratio is expressed as: Where: f L-max The distance from point D to the center of rotation of the rotor blade is L OD The maximum frequency shift produced when It can be determined from formula (11) that the length-to-width ratio of the rotor blade is equivalent to the ratio of the corresponding micro-Doppler maximum frequency shift, which is independent of the pitch angle parameter; Step 42: Error analysis of rotor blade aspect ratio based on sub-frequency band characteristics; Since the absolute error of the estimated aspect ratio is approximately in an exponential relationship with the true aspect ratio, when the aspect ratio does not exceed 6:1, the absolute error increases rapidly as the aspect ratio increases. After the aspect ratio exceeds 8:1, it fluctuates around 0.17, and the maximum absolute error does not exceed 0.2; the relative error fluctuates between 1% and 2.2%, and is less than 2.2%.

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